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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrdbourke/tensorflow-deep-learning/blob/main/video_notebooks/07_milestone_project_1_food_vision_video.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWlc4fR-8bsg"
      },
      "source": [
        "# Milestone Project 1: Food Vision Big\n",
        "\n",
        "See the annotated version (plenty of text and explanations for each part) of this notebook on GitHub: https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/07_food_vision_milestone_project_1.ipynb\n",
        "\n",
        "And see the full course materials (including exercises and extra-curriculum) on GitHub: https://github.com/mrdbourke/tensorflow-deep-learning/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xoQTvXWL8tHa"
      },
      "source": [
        "## Check GPU\n",
        "\n",
        "Google Colab offers free GPUs (thank you Google), however, not all of them are compatiable with mixed precision training.\n",
        "\n",
        "Google Colab offers:\n",
        "* K80 (not compatible)\n",
        "* P100 (not compatible) \n",
        "* Tesla T4 (compatible)\n",
        "\n",
        "Knowing this, in order to use mixed precision training we need access to a Tesla T4 (from within Google Colab) or if we're using our own hardware, our GPU needs a score of 7.0+ (see here: https://developer.nvidia.com/cuda-gpus).\n",
        "\n",
        "📖 **Resource:** You can read more about the benefits of mixed precision training in the TensorFlow Mixed Precision documentation: https://www.tensorflow.org/guide/mixed_precision \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6mnf_0-8vp_",
        "outputId": "9c76c014-b87e-4534-8416-4548056f299d"
      },
      "source": [
        "# If the following line doesn't output \"Tesla T4\", you can try getting access to\n",
        "# another GPU by going to Runtime -> Factory Reset Runtime -> \"Yes\" and then\n",
        "# re-running this cell.\n",
        "!nvidia-smi -L"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU 0: Tesla T4 (UUID: GPU-2e784939-8fa3-af9b-d664-8d1cf94e149a)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ns-X9wi6-U1w"
      },
      "source": [
        "## Get helper functions\n",
        "\n",
        "In past modules, we've created a bunch of helper functions to do small tasks required for our notebooks.\n",
        "\n",
        "Rather than rewrite all of these, we can import a script and load them in from there.\n",
        "\n",
        "The script we've got available can be found on GitHub: https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJRk1sOm_pQC",
        "outputId": "fe2254e7-072f-47c5-891e-6d6b193e839d"
      },
      "source": [
        "# Download helper functions script\n",
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-24 01:27:33--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 9304 (9.1K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "\rhelper_functions.py   0%[                    ]       0  --.-KB/s               \rhelper_functions.py 100%[===================>]   9.09K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-03-24 01:27:33 (92.7 MB/s) - ‘helper_functions.py’ saved [9304/9304]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjhvILRy_-r0"
      },
      "source": [
        "# Import series of helper functions for the notebook\n",
        "from helper_functions import create_tensorboard_callback, plot_loss_curves, compare_historys"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCPMb7ZxAQbM"
      },
      "source": [
        "## Use TensorFlow Datasets to Download Data\n",
        "\n",
        "If you want to get an overview of TensorFlow Datasets (TFDS), read the guide: https://www.tensorflow.org/datasets/overview"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2KmgCHCJA3r5"
      },
      "source": [
        "# Get TensorFlow Datasets\n",
        "import tensorflow_datasets as tfds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDqtroSyBq9T",
        "outputId": "53be1896-53c3-4c43-a304-320af0605634"
      },
      "source": [
        "# List all available datasets\n",
        "datasets_list = tfds.list_builders() # get all available datasets in TFDS\n",
        "print(\"food101\" in datasets_list) # is our target dataset in the list of TFDS datasets?"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWRZJHIXD2ak"
      },
      "source": [
        "🛠 **Exercise:** Running the following cell can take a while using Google Colab (about 5-6 minutes). While your data is downloading, have a read through the TensorFlow Datasets documentation: https://www.tensorflow.org/datasets/overview\n",
        "\n",
        "🔑 **Note:** Some of the datasets contained within TensorFlow datasets are over 100GB+, meaning if you run the cell below (outside of Google Colab), you might be downloading 100GB+ to your computer or the computer that you're using. Beware as this could take a large amount of bandwidth."
      ]
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            "6b659db66e384424bb520f7560280351",
            "d1800d2ffddc4e748c06e81caa9b3a0c",
            "28144711ea794b1bb13879375734a0c8",
            "74a06a42ecb84ba78a2e5666c68da3b7",
            "be6600013bcb42ef88288fd5c07d929d"
          ]
        },
        "id": "SrrpBuImB31p",
        "outputId": "0bcfbb66-1819-420e-fa68-e7689da1c5a4"
      },
      "source": [
        "# Load in the data (takes 5-6 minutes in Google Colab)\n",
        "(train_data, test_data), ds_info = tfds.load(name=\"food101\",\n",
        "                                             split=[\"train\", \"validation\"], # splits can be a little tricky, for more see: https://www.tensorflow.org/datasets/splits \n",
        "                                             shuffle_files=True,\n",
        "                                             as_supervised=True, # data gets returned in tuple format (data, label)\n",
        "                                             with_info=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset food101/2.0.0 (download: 4.65 GiB, generated: Unknown size, total: 4.65 GiB) to /root/tensorflow_datasets/food101/2.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6236528af514392af569cc2617a693e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f418fdb7441d4914bca603a4da56b484",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/food101/2.0.0.incompleteVTQ865/food101-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
        {
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          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/food101/2.0.0.incompleteVTQ865/food101-validation.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            ]
          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset food101 downloaded and prepared to /root/tensorflow_datasets/food101/2.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7a8wfDP9Gaa6"
      },
      "source": [
        "## Exploring the Food101 data from TensorFlow Datasets\n",
        "\n",
        "To become one with our data, we want to find:\n",
        "* Class names\n",
        "* The shape of our input data (image tensors)\n",
        "* The datatype of our input data\n",
        "* What the labels look like (e.g. are they one-hot encoded or are they label encoded)\n",
        "* Do the labels match up with the class names?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7Mj3J2ZC_Ed",
        "outputId": "b493d2e6-17cd-423c-ad0c-22cd4c2dff52"
      },
      "source": [
        "# Features of Food101 from TFDS\n",
        "ds_info.features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FeaturesDict({\n",
              "    'image': Image(shape=(None, None, 3), dtype=tf.uint8),\n",
              "    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=101),\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63vFfg3tGCaR",
        "outputId": "ee0ff41a-1b40-4eeb-f2ee-6cbb0ff0786e"
      },
      "source": [
        "# Get the class names\n",
        "class_names = ds_info.features[\"label\"].names\n",
        "class_names[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apple_pie',\n",
              " 'baby_back_ribs',\n",
              " 'baklava',\n",
              " 'beef_carpaccio',\n",
              " 'beef_tartare',\n",
              " 'beet_salad',\n",
              " 'beignets',\n",
              " 'bibimbap',\n",
              " 'bread_pudding',\n",
              " 'breakfast_burrito']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooGi930eHGCr"
      },
      "source": [
        "# Take one sample of the train data\n",
        "train_one_sample = train_data.take(1) # samples are in format (image_tensor, label)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rJzB-f7HMAk",
        "outputId": "bfdb20db-d3ae-463e-c921-232412defc1f"
      },
      "source": [
        "# What does one sample of our training data look like?\n",
        "train_one_sample"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<TakeDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6EHedePqHbaY",
        "outputId": "55f1a4d7-a2f1-4824-8f33-5e17450c305b"
      },
      "source": [
        "# Output info about our training sample\n",
        "for image, label in train_one_sample:\n",
        "  print(f\"\"\"\n",
        "  Image shape: {image.shape}\n",
        "  Image datatype: {image.dtype}\n",
        "  Target class from Food101 (tensor form): {label}\n",
        "  Class name (str form): {class_names[label.numpy()]}\n",
        "  \"\"\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  Image shape: (384, 512, 3)\n",
            "  Image datatype: <dtype: 'uint8'>\n",
            "  Target class from Food101 (tensor form): 97\n",
            "  Class name (str form): takoyaki\n",
            "  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stxlJZRXILTG",
        "outputId": "b5c1a8c0-d130-4813-b6f5-1f698c87ea41"
      },
      "source": [
        "# What does our image tensor from TFDS's Food101 look like?\n",
        "image"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(384, 512, 3), dtype=uint8, numpy=\n",
              "array([[[ 54,  30,  26],\n",
              "        [ 57,  33,  29],\n",
              "        [ 72,  49,  43],\n",
              "        ...,\n",
              "        [192, 184, 163],\n",
              "        [190, 179, 159],\n",
              "        [181, 169, 147]],\n",
              "\n",
              "       [[ 51,  28,  22],\n",
              "        [ 60,  37,  31],\n",
              "        [ 81,  58,  52],\n",
              "        ...,\n",
              "        [191, 183, 162],\n",
              "        [189, 176, 157],\n",
              "        [176, 164, 142]],\n",
              "\n",
              "       [[ 78,  58,  49],\n",
              "        [ 96,  76,  67],\n",
              "        [122, 102,  93],\n",
              "        ...,\n",
              "        [191, 183, 162],\n",
              "        [188, 175, 156],\n",
              "        [172, 157, 136]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[162, 140, 117],\n",
              "        [164, 142, 119],\n",
              "        [169, 147, 126],\n",
              "        ...,\n",
              "        [  5,   5,   5],\n",
              "        [  6,   6,   6],\n",
              "        [  6,   6,   6]],\n",
              "\n",
              "       [[168, 146, 123],\n",
              "        [170, 148, 125],\n",
              "        [176, 154, 131],\n",
              "        ...,\n",
              "        [  5,   5,   5],\n",
              "        [  6,   6,   6],\n",
              "        [  6,   6,   6]],\n",
              "\n",
              "       [[173, 151, 128],\n",
              "        [176, 154, 131],\n",
              "        [182, 160, 137],\n",
              "        ...,\n",
              "        [  4,   4,   4],\n",
              "        [  6,   6,   6],\n",
              "        [  6,   6,   6]]], dtype=uint8)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFdkOB59IQBe",
        "outputId": "61c9fc9d-2302-422b-b399-0482c915a6fa"
      },
      "source": [
        "# What are the min and max values of our image tensor?\n",
        "import tensorflow as tf\n",
        "tf.reduce_min(image), tf.reduce_max(image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=uint8, numpy=0>,\n",
              " <tf.Tensor: shape=(), dtype=uint8, numpy=255>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfVA5M1CIa2m"
      },
      "source": [
        "### Plot an image from TensorFlow Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "WNzDdVoeIny4",
        "outputId": "2426e0fc-956f-4343-b7b3-e47a85125e66"
      },
      "source": [
        "# Plot an image tensor\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(image)\n",
        "plt.title(class_names[label.numpy()]) # Add title to image to verify the label is assosciated with the right image\n",
        "plt.axis(False);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wxb7hBF0JF0m"
      },
      "source": [
        "## Create preprocessing functions for our data\n",
        "\n",
        "Neural networks perform best when data is in a certain way (e.g. batched, normalized, etc).\n",
        "\n",
        "However, not all data (including data from from TensorFlow Datasets) comes like this.\n",
        "\n",
        "So in order to get it ready for a neural network, you'll often have to write preprocessing functions and map it to your data.\n",
        "\n",
        "What we know about our data:\n",
        "* In `uint8` datatype\n",
        "* Comprised of all different size tensors (different sized images)\n",
        "* Not scaled (the pixel values are between 0 & 255)\n",
        "\n",
        "What we know models like:\n",
        "* Data in `float32` dtype (or for mixed precision `float16` and `float32`)\n",
        "* For batches, TensorFlow likes all of the tensors within a batch to be of the same size\n",
        "* Scaled (values between 0 & 1) also called normalized tensors generally perform better\n",
        "\n",
        "With these points in mind, we've got a few things we can tackle with a preprocessing function.\n",
        "\n",
        "Since we're going to be using an EfficientNetBX pretrained model from tf.keras.applications we don't need to rescale our data (these architectures have rescaling built-in).\n",
        "\n",
        "This means our functions needs to:\n",
        "1. Reshape our images to all the same size\n",
        "2. Convert the dtype of our image tensors from `unit8` to `float32`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gD5e7KtxMJMD"
      },
      "source": [
        "# Our data comes in the tuple structure: (image, label)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDxSaBCRKU4C"
      },
      "source": [
        "# Make a function for preprocessing images\n",
        "def preprocess_img(image, label, img_shape=224):\n",
        "  \"\"\"\n",
        "  Converts image datatype from 'uint8' -> 'float32' and reshapes\n",
        "  image to [img_shape, img_shape, colour_channels]\n",
        "  \"\"\"\n",
        "  image = tf.image.resize(image, [img_shape, img_shape]) # reshape target image\n",
        "  # image = image/255. # scale image values (not required with EfficientNetBX models from tf.keras.applications)\n",
        "  return tf.cast(image, tf.float32), label # return (float32_image, label) tuple"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWYDqRsxMtbf",
        "outputId": "a314bb4f-ddc0-4553-f31b-092c3f6d0ab8"
      },
      "source": [
        "# Preprocess a single sample image and check the outputs\n",
        "preprocessed_img = preprocess_img(image, label)[0]\n",
        "print(f\"Image before preprocessing:\\n {image[:2]}..., \\nShape: {image.shape},\\nDatatype: {image.dtype}\\n\")\n",
        "print(f\"Image after preprocessing:\\n{preprocessed_img[:2]}...,\\nShape: {preprocessed_img.shape},\\nDatatype: {preprocessed_img.dtype}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image before preprocessing:\n",
            " [[[ 54  30  26]\n",
            "  [ 57  33  29]\n",
            "  [ 72  49  43]\n",
            "  ...\n",
            "  [192 184 163]\n",
            "  [190 179 159]\n",
            "  [181 169 147]]\n",
            "\n",
            " [[ 51  28  22]\n",
            "  [ 60  37  31]\n",
            "  [ 81  58  52]\n",
            "  ...\n",
            "  [191 183 162]\n",
            "  [189 176 157]\n",
            "  [176 164 142]]]..., \n",
            "Shape: (384, 512, 3),\n",
            "Datatype: <dtype: 'uint8'>\n",
            "\n",
            "Image after preprocessing:\n",
            "[[[ 56.23469   32.591835  27.877552]\n",
            "  [ 98.16328   75.16328   68.50001 ]\n",
            "  [121.62245  101.62245   91.63265 ]\n",
            "  ...\n",
            "  [191.72456  191.36736  170.93883 ]\n",
            "  [202.18863  196.9743   176.90286 ]\n",
            "  [185.918    174.10167  153.6169  ]]\n",
            "\n",
            " [[ 93.47959   73.55102   64.19388 ]\n",
            "  [144.57143  124.64286  113.561226]\n",
            "  [169.66327  150.20409  138.79082 ]\n",
            "  ...\n",
            "  [196.22955  195.22955  176.80103 ]\n",
            "  [202.02025  194.9335   175.87225 ]\n",
            "  [182.24942  168.44324  148.72888 ]]]...,\n",
            "Shape: (224, 224, 3),\n",
            "Datatype: <dtype: 'float32'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZZOb2mWNNa8"
      },
      "source": [
        "## Batch & prepare datasets\n",
        "\n",
        "We're now going to make our data input pipeline run really fast. \n",
        "\n",
        "For more resources on this, I'd highly going through the following guide: https://www.tensorflow.org/guide/data_performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7KCYKnAq5JmW"
      },
      "source": [
        "# Map preprocessing function to training (and parallelize)\n",
        "train_data = train_data.map(map_func=preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "# Shuffle train_data and turn it into batches and prefetch it (load it faster)\n",
        "train_data = train_data.shuffle(buffer_size=1000).batch(batch_size=32).prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "\n",
        "# Map preprocessing function to test data\n",
        "test_data = test_data.map(preprocess_img, num_parallel_calls=tf.data.AUTOTUNE).batch(32).prefetch(tf.data.AUTOTUNE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHDQoLS55tBQ",
        "outputId": "ab4d62ad-4c8e-4007-81ae-47522b029938"
      },
      "source": [
        "train_data, test_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<PrefetchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int64)>,\n",
              " <PrefetchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int64)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SUZ9qtJ76fX"
      },
      "source": [
        "> \"Hey, TensorFlow, map this preprocessing function (`preprocess_img`) across our training dataset, then shuffle a number of elements and then batch them together and finally make sure you prepare new batches (prefetch) whilst the model is looking through (finding patterns) the current batch.\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwAuHdD-8-xa"
      },
      "source": [
        "## Create modelling callbacks\n",
        "\n",
        "We're going to create a couple of callbacks to help us while our model trains:\n",
        "* TensorBoard callback to log training results (so we can visualize them later if need be)\n",
        "* ModelCheckpoint callback to save our model's progress after feature extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8shN8c26-mqS"
      },
      "source": [
        "# Create tensorboard callback (import from helper_functions.py)\n",
        "from helper_functions import create_tensorboard_callback\n",
        "\n",
        "# Create ModelCheckpoint callback to save a model's progress during training \n",
        "checkpoint_path = \"model_checkpoints/cp.ckpt\"\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n",
        "                                                      montior=\"val_acc\",\n",
        "                                                      save_best_only=True,\n",
        "                                                      save_weights_only=True,\n",
        "                                                      verbose=0) # don't print whether or not model is being saved"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Exuur99L_d-w"
      },
      "source": [
        "## Setup mixed precision training\n",
        "\n",
        "First and foremost, for a deeper understanding of mixed precision training, check out the TensorFlow guide for mixed precision: https://www.tensorflow.org/guide/mixed_precision\n",
        "\n",
        "Mixed precision utilizes a combination of float32 and float16 data types to speed up model performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDt0YisgA20e",
        "outputId": "11ef31a3-d1f9-4b65-c6e4-a340120f511c"
      },
      "source": [
        "# Turn on mixed precision training\n",
        "from tensorflow.keras import mixed_precision\n",
        "mixed_precision.set_global_policy(\"mixed_float16\") # set global data policy to mixed precision "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK\n",
            "Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: Tesla T4, compute capability 7.5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK\n",
            "Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: Tesla T4, compute capability 7.5\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_M8FpRk5Bsqn",
        "outputId": "8e5e3ebb-d7ad-4b06-e04c-c9bd0c081fa4"
      },
      "source": [
        "mixed_precision.global_policy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Policy \"mixed_float16\">"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MWzUcgYCXJQ"
      },
      "source": [
        "## Build feature extraction model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VODMgH-FB9y",
        "outputId": "8a3e1a7b-687c-4b6f-f147-8b039eb15408"
      },
      "source": [
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "\n",
        "# Create base model\n",
        "input_shape = (224, 224, 3)\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create functional model\n",
        "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "# Note: EfficientNetBX models have rescaling built-in but if your model doesn't you can have a layer like below\n",
        "# x = preprocessing.Rescaling(1./255)(x)\n",
        "x = base_model(inputs, training=False) # makes sure layers which should be in inference mode only stay like that\n",
        "x = layers.GlobalAveragePooling2D()(x)\n",
        "x = layers.Dense(len(class_names))(x)\n",
        "outputs = layers.Activation(\"softmax\", dtype=tf.float32, name=\"softmax_float32\")(x)\n",
        "model = tf.keras.Model(inputs, outputs) \n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=tf.keras.optimizers.Adam(),\n",
        "              metrics=[\"accuracy\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16711680/16705208 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnsSvKMoGA9M",
        "outputId": "5b05e4c6-7b91-4136-ac68-aefbd04be42d"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_layer (InputLayer)     [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "efficientnetb0 (Functional)  (None, None, None, 1280)  4049571   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 101)               129381    \n",
            "_________________________________________________________________\n",
            "softmax_float32 (Activation) (None, 101)               0         \n",
            "=================================================================\n",
            "Total params: 4,178,952\n",
            "Trainable params: 129,381\n",
            "Non-trainable params: 4,049,571\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mnUh7OQiGbQc"
      },
      "source": [
        "## Checking layer dtype policies (are we using mixed precision?)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aKpd1fQIoma",
        "outputId": "202d8485-249c-4de8-a39d-0cc307ecc786"
      },
      "source": [
        "# Check the dtype_policy attributes of layers in our model\n",
        "for layer in model.layers:\n",
        "  print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_layer True float32 <Policy \"float32\">\n",
            "efficientnetb0 False float32 <Policy \"mixed_float16\">\n",
            "global_average_pooling2d True float32 <Policy \"mixed_float16\">\n",
            "dense True float32 <Policy \"mixed_float16\">\n",
            "softmax_float32 True float32 <Policy \"float32\">\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eaqT9DxI-SH"
      },
      "source": [
        "Going through the above we see:\n",
        "* `layer.name`: the human readable name of a particular layer\n",
        "* `layer.trainable`: is the layer trainable or not? (if `False`, the weights are frozen)\n",
        "* `layer.dtype`: the data type a layer stores its variables in\n",
        "* `layer.dtype_policy`: the data type policy a layer computes on its variables with"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ek8_JHCxI6fS",
        "outputId": "6d1264fc-cdc6-44eb-a9f7-39e0048b9215"
      },
      "source": [
        "# Check the dtype_policy attributes of layers in the base model\n",
        "for layer in model.layers[1].layers[:20]: # check the layers of the base model (layer at index 1 of `model`)\n",
        "  print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_1 False float32 <Policy \"float32\">\n",
            "rescaling False float32 <Policy \"mixed_float16\">\n",
            "normalization False float32 <Policy \"float32\">\n",
            "stem_conv_pad False float32 <Policy \"mixed_float16\">\n",
            "stem_conv False float32 <Policy \"mixed_float16\">\n",
            "stem_bn False float32 <Policy \"mixed_float16\">\n",
            "stem_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv False float32 <Policy \"mixed_float16\">\n",
            "block1a_bn False float32 <Policy \"mixed_float16\">\n",
            "block1a_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_activation False float32 <Policy \"mixed_float16\">\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BGUMwq9sJ2DS",
        "outputId": "5d7a8fc3-a506-4cc7-e75f-906fc3dd5470"
      },
      "source": [
        "mixed_precision.global_policy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Policy \"mixed_float16\">"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHKdhRKMKVNk"
      },
      "source": [
        "## Fit the feature extraction model\n",
        "\n",
        "If our goal is to fine-tune a pretrained model, the general order of doing things is:\n",
        "1. Build a feature extraction model (train a couple output layers with base layers frozen) \n",
        "2. Fine-tune some of the frozen layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NjAEEJ4K0M7",
        "outputId": "86a44640-6f93-48c0-f687-50b3d0ada149"
      },
      "source": [
        "# Fit the feature extraction model with callbacks\n",
        "history_101_food_classes_feature_extract = model.fit(train_data,\n",
        "                                                     epochs=3,\n",
        "                                                     steps_per_epoch=(len(train_data)),\n",
        "                                                     validation_data=test_data,\n",
        "                                                     validation_steps=int(0.15 * len(test_data)),\n",
        "                                                     callbacks=[create_tensorboard_callback(dir_name=\"training_logs\",\n",
        "                                                                                            experiment_name=\"efficientnetb0_101_classes_all_data_feature_extract\"),\n",
        "                                                                model_checkpoint])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving TensorBoard log files to: training_logs/efficientnetb0_101_classes_all_data_feature_extract/20210324-030405\n",
            "Epoch 1/3\n",
            "2368/2368 [==============================] - 227s 80ms/step - loss: 2.3255 - accuracy: 0.4661 - val_loss: 1.2176 - val_accuracy: 0.6793\n",
            "Epoch 2/3\n",
            "2368/2368 [==============================] - 161s 67ms/step - loss: 1.3196 - accuracy: 0.6610 - val_loss: 1.1180 - val_accuracy: 0.7037\n",
            "Epoch 3/3\n",
            "2368/2368 [==============================] - 160s 67ms/step - loss: 1.1405 - accuracy: 0.7057 - val_loss: 1.0874 - val_accuracy: 0.7058\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ag0jN0HyMG7-",
        "outputId": "26ec82be-0dca-4c3c-de91-744dee5dea2e"
      },
      "source": [
        "# Evaluate model on whole test dataset\n",
        "results_feature_extract_model = model.evaluate(test_data)\n",
        "results_feature_extract_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "790/790 [==============================] - 52s 66ms/step - loss: 1.0863 - accuracy: 0.7079\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0863139629364014, 0.7079207897186279]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMu2lCgMOfYf"
      },
      "source": [
        "## Off to you... \n",
        "\n",
        "We've covered a lot of ground so far but since this is a milestone project, it's time for you to takeover.\n",
        "\n",
        "More specifically, you're challenge is to complete the TEMPLATE version of 07 to obtain a computer vision model (building off the one we've built in this notebook) to beat the DeepFood paper.\n",
        "\n",
        "In other words, you're going to create Food Vision Big!!!\n",
        "\n",
        "You can find the TEMPLATE notebook here: https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/extras/TEMPLATE_07_food_vision_milestone_project_1.ipynb\n",
        "\n",
        "And if you get stuck, you can refer to the example version (non-template): https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/07_food_vision_milestone_project_1.ipynb"
      ]
    }
  ]
}